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About This Role
About the Role
We are looking for a Senior Data Scientist to develop data-driven forecasting and analytics solutions for networking hardware and data center infrastructure planning.
This role sits at the intersection of data science, demand forecasting, planning, and cloud infrastructure. You will work with large and complex datasets to understand demand drivers, develop forecasting models, improve the quality of the demand signal, and provide analytical insights that support hardware procurement, capacity planning, and data center build decisions.
The role is highly hands-on. You will work closely with Demand Planning, Networking Engineering, Data Center Planning, Supply Chain, Product, and Finance teams to turn business and engineering questions into analytical models and actionable insights.
- Develop and maintain demand forecasting models for networking hardware and data center infrastructure across short- and long-term planning horizons.
- Analyze historical demand, deployment trends, data center build plans, engineering inputs, product roadmaps, and other signals to identify key demand drivers.
- Develop time-series, statistical, machine learning, and other forecasting approaches appropriate for different products and planning scenarios.
- Measure and improve forecast accuracy, bias, stability, and confidence across products and planning horizons.
- Build analytical models to evaluate base, upside, downside, and what-if scenarios and quantify their impact on hardware demand.
- Partner with demand planners to translate analytical outputs into practical planning recommendations and improve forecasting processes.
- Develop models that connect data center builds, deployment plans, network capacity, product requirements, BOMs, and SKU-level demand.
- Investigate anomalies and changes in the demand signal, including unexpected demand shifts, duplicate signals, missing data, and inconsistent planning assumptions.
- Build scalable datasets and analytical pipelines using SQL and Python to support forecasting and planning workflows.
- Develop reusable analytical frameworks, dashboards, and tools that improve visibility into demand trends, forecast performance, and planning risks.
- Work with Data Engineering and other technical teams to improve data quality, data availability, and the reliability of planning data.
- Automate recurring analysis, forecasting, reconciliation, and reporting processes.
- Apply machine learning, optimization, simulation, and AI/ML techniques where they provide meaningful improvements to planning or decision-making.
- Communicate analytical findings clearly to planners, engineers, business partners, and senior leadership.
- Work independently on ambiguous problems, define the appropriate analytical approach, and drive projects from problem definition through implementation and adoption.
- Contribute to the development of modern, scalable data science and forecasting capabilities for demand planning.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Operations Research, Economics, or a related quantitative field.
- 5+ years of experience in data science, applied science, forecasting, demand planning analytics, operations research, or a related quantitative discipline.
- Strong hands-on programming experience with Python and advanced SQL.
- Experience developing and deploying forecasting or predictive models using real-world business data.
- Strong understanding of statistical modeling, time-series forecasting, model evaluation, and experimental or analytical methodologies.
- Experience working with large and complex datasets and performing data exploration, feature engineering, modeling, and analysis.
- Experience translating business or operational problems into quantitative models and actionable recommendations.
- Experience working with planning, supply chain, capacity planning, demand forecasting, or other operational decision-making processes.
- Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders.
Preferred Qualifications
- Experience with demand planning or forecasting for hardware, cloud infrastructure, networking, semiconductor, or data center environments.
- Experience working with SKU-level demand, BOMs, product lifecycles, NPI, EOL/EOS, or hardware product transitions.
- Experience connecting data center build plans or deployment schedules to hardware demand forecasts.
- Experience with networking hardware such as switches, NICs, DPUs/SmartNICs, optical transceivers, cables, or related components.
- Experience with advanced forecasting methods including probabilistic forecasting, hierarchical forecasting, causal modeling, gradient boosting, deep learning, or other machine learning techniques.
- Experience with scenario modeling, optimization, simulation, or capacity planning.
- Experience building production-quality data pipelines, analytical datasets, or forecasting platforms.
- Experience with cloud data platforms, distributed data processing, or large-scale analytics.
- Experience applying GenAI/LLMs or AI agents to analytics, forecasting, or planning workflows.
- Experience communicating analytical results and recommendations to senior engineering, business, or executive audiences.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Oracle AI Hiring
Oracle has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Nashville, TN, US, Santa Clara, CA, US, Nashville, TN.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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